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Comparison of Intfloat: Multilingual-E5-Large, Intfloat: E5-Base-v2, Together: Tev1 4B Experimental
3 modelsintfloatIntfloat: Multilingual-E5-LargeRemove ×intfloatIntfloat: E5-Base-v2Remove ×togethercomputerTogether: Tev1 4B ExperimentalRemove ×
What’s differentThe multilingual-e5-large embedding model encodes sentences, paragraphs, and documents across over 90 languages into a 1024-dimensional dense vector space, delivering robust semantic embeddings optimized…The e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as…Tev1 4B Experimental is an experimental decision model from Together AI, a supervised fine-tune of Qwen3.5-4B trained to choose one option from a structured state, question, and list of 2-24...
DateJun 4, 2024ListedNov 22, 2023ListedSep 30, 2026Listed
Listed rate$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.042 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗
Provider endpoints1 endpoint →1 endpoint →1 endpoint →
Context window51251232.8K
Max output4604608
Parameters——4B
Open weightsSupportedSupportedSupported

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